Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

Luigi
/
sloth-ime-models

Token Classification
GGUF
Safetensors
Chinese
input-method
zhuyin
bopomofo
traditional-chinese
ternary
bitnet
Model card Files Files and versions
xet
Community

Instructions to use Luigi/sloth-ime-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use Luigi/sloth-ime-models with llama.cpp:

    Install (macOS, Linux)
    curl -LsSf https://llama.app/install.sh | sh
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf Luigi/sloth-ime-models
    # Run inference directly in the terminal:
    llama cli -hf Luigi/sloth-ime-models
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf Luigi/sloth-ime-models
    # Run inference directly in the terminal:
    llama cli -hf Luigi/sloth-ime-models
    Use pre-built binary
    # Download pre-built binary from:
    # https://github.com/ggerganov/llama.cpp/releases
    # Start a local OpenAI-compatible server with a web UI:
    ./llama-server -hf Luigi/sloth-ime-models
    # Run inference directly in the terminal:
    ./llama-cli -hf Luigi/sloth-ime-models
    Build from source code
    git clone https://github.com/ggerganov/llama.cpp.git
    cd llama.cpp
    cmake -B build
    cmake --build build -j --target llama-server llama-cli
    # Start a local OpenAI-compatible server with a web UI:
    ./build/bin/llama-server -hf Luigi/sloth-ime-models
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf Luigi/sloth-ime-models
    Use Docker
    docker model run hf.co/Luigi/sloth-ime-models
  • LM Studio
  • Jan
  • Ollama

    How to use Luigi/sloth-ime-models with Ollama:

    ollama run hf.co/Luigi/sloth-ime-models
  • Unsloth Studio

    How to use Luigi/sloth-ime-models with Unsloth Studio:

    Install Unsloth Studio (macOS, Linux, WSL)
    curl -fsSL https://unsloth.ai/install.sh | sh
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for Luigi/sloth-ime-models to start chatting
    Install Unsloth Studio (Windows)
    irm https://unsloth.ai/install.ps1 | iex
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for Luigi/sloth-ime-models to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for Luigi/sloth-ime-models to start chatting
  • Docker Model Runner

    How to use Luigi/sloth-ime-models with Docker Model Runner:

    docker model run hf.co/Luigi/sloth-ime-models
  • Lemonade

    How to use Luigi/sloth-ime-models with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull Luigi/sloth-ime-models
    Run and chat with the model
    lemonade run user.sloth-ime-models-{{QUANT_TAG}}
    List all available models
    lemonade list
  • Atomic Chat
sloth-ime-models / pred_q35_60m
121 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
Luigi's picture
Luigi
predictor v2.1 full weights (TW-chat register FT)
0a8952e verified 20 days ago
  • config.json
    1.12 kB
    decoder full weights: 60M dense-Qwen3.5 GDN next-word predictor (HF format, fp32 safetensors + tokenizer) 21 days ago
  • generation_config.json
    195 Bytes
    decoder full weights: 60M dense-Qwen3.5 GDN next-word predictor (HF format, fp32 safetensors + tokenizer) 21 days ago
  • model.safetensors
    120 MB
    xet
    predictor v2.1 full weights (TW-chat register FT) 20 days ago
  • tokenizer.json
    1.26 MB
    decoder full weights: 60M dense-Qwen3.5 GDN next-word predictor (HF format, fp32 safetensors + tokenizer) 21 days ago